In recent years, the adoption of cloud services has been expanding at an unprecedented rate. As more and more organizations migrate or deploy their businesses to the cloud, a multitude of related cybersecurity incidents such as data breaches are on the rise. Many inherent attributes of cloud environments, for example, data sharing, remote access, dynamicity and scalability, pose significant challenges for the protection of cloud security. Even worse, cyber threats are becoming increasingly sophisticated and covert. Attack methods, such as Advanced Persistent Threats (APTs), are continually developed to bypass traditional security measures. Among the emerging technologies for robust threat detection, system provenance analysis is being considered as a promising mechanism, thus attracting widespread attention in the field of incident response. This paper proposes a new few-shot learning-based attack detection with improved data context intelligence. We collect operating system behavior data of cloud systems during realistic attacks and leverage an innovative semiotics extraction method to describe system events. Inspired by the advances in semantic analysis, which is a fruitful area focused on understanding natural languages in computational linguistics, we further convert the anomaly detection problem into a similarity comparison problem. Comprehensive experiments show that the proposed approach is able to generalize over unseen attacks and make accurate predictions, even if the incident detection models are trained with very limited samples.

In this study, we explore the role of contextual information in enhancing ChatGPT's capabilities in bug fixing. Our focus is specifically on the “Wrong Answer” problem, where a program executes without error but fails to produce the correct output. Our approach draws inspiration from human debugging practices, which heavily rely on understanding both the intended task of the program and the specific scenarios in which it fails, such as unit test cases. We evaluate ChatGPT's performance with various types and levels of contextual data. The results reveal three key insights. First, providing the model with a mix of correct and incorrect test cases sharpens its debugging skills. Second, giving ChatGPT detailed descriptions of the problems substantially enhances its ability to identify and resolve errors. Third, merging detailed problem descriptions with various test cases leads to a synergistic outcome. This combined approach significantly elevates the efficiency of the bug-fixing process compared to employing each type of contextual information individually. Our paper presents a thorough analysis based on these findings. It offers an extensive exploration of why and how contextual information can be strategically utilized to enhance ChatGPT's debugging effectiveness. Furthermore, this investigation enriches our comprehension of the underlying mechanisms by which contextual cues amplify the model's capacity for solving problems.

In this study, we present a novel attention-based encoder-decoder model for monocular depth estimation, show-casing exceptional robustness and accuracy across standard and noise-injected variants of the KITTI dataset. By inte-grating Convolutional Block Attention Module (CBAM) and Squeeze-and-Excitation (SE) blocks, our approach significantly outperforms existing state-of-the-art methods, especially in en-vironments affected by real-world noise such as changes in brightness, saturation, and RGB channels. The model's superior performance, validated through rigorous testing, marks a signif-icant step forward in the field, offering promising applications in autonomous driving, augmented reality, and beyond. Our work demonstrates the potential of attention mechanisms in enhancing depth estimation models to reliably interpret complex scenes, paving the way for advancements in depth-dependent technologies operating in dynamic and challenging conditions.

Each Department of Transportation in the United States must provide to the Federal Highway Administration on annual basis the number and types of vehicles traveled on its state-maintained roads. These data are fed into the Highway Performance Monitoring System used to assess the nation’s highway system performance. Classifying vehicles (i.e., identifying their types, e.g., passenger cars, trucks, etc.) during nighttime is quite challenging due to limited lighting. This study designed and evaluated three Convolutional Neural Network (CNN) models to classify vehicles using their thermal images. These three models have architectures that differ in the number of layers and, in the case of the third model, the addition of an inception layer. Of these, the second model achieves the best performance, achieving mean accuracy scores of greater than 97% for each of the three vehicle classes and f1 scores of greater than 98%. We proposed two training-test methods based on data augmentation to avoid over-fitting and to improve performance. The experimental results demonstrated that a data augmentation training-test method improves model performance further with regard to both accuracy and f1-score.

With the proliferation of smart devices, children can be easily exposed to violent or adult-only content on the Internet. Without any precaution, the premature and unsupervised use of smart devices can be harmful to both children and their parents. Thus, it is critical to employ parent patrol mechanisms such that children are restricted to child-friendly content only. A successful parent patrol strategy has to be user friendly and privacy aware. The apps that require explicit actions from parents are not effective because a parent may forget to enable them, and the ones that use built-in cameras or microphones to detect child users may impose privacy violations. In this article, we propose iCare, a system that can identify child users automatically and seamlessly when users operate smartphones. In particular, iCare investigates the intrinsic differences of screen-touch patterns between child and adult users from the aspect of physiological maturity. We discover that one’s touch behaviors are related to his or her age. Thus, iCare records the touch behaviors and extracts hand geometry, finger dexterity, and hand stability features that capture the age information. We conduct experiments on 100 people including 62 children (3 to 17 years old) and 38 adults (18 to 59 years old). Results show that iCare can achieve 96.6% accuracy for child identification using only a single swipe on the screen, and the accuracy becomes 98.3% with three consecutive swipes.

Real-time bidding (RTB) that features perimpression-level real-time ad auctions has become a popular practice in today's digital advertising industry. In RTB, click-through rate (CTR) prediction is a fundamental problem to ensure the success of an ad campaign and boost revenue. In this paper, we present a dynamic CTR prediction model designed for the Samsung demand-side platform (DSP). From our production data, we identify two key technical challenges that have not been fully addressed by the existing solutions: the dynamic nature of RTB and user information scarcity. To address both challenges, we develop a Dynamic Neural Network model. Our model effectively captures the dynamic evolutions of both users and ads and integrates auxiliary data sources (e.g., installed apps) to better model users' preferences. We put forward a novel interaction layer that fuses both explicit user responses (e.g., clicks on ads) and auxiliary data sources to generate consolidated user preference representations. We evaluate our model using a large amount of data collected from the Samsung advertising platform and compare our method against several state-of-the-art methods that are likely suitable for real-world deployment. The evaluation results demonstrate the effectiveness of our method and the potential for production. In addition, we discuss how to address a few practical engineering challenges caused by big data toward making our model in readiness for deployment.